Carotid vulnerable plaque in coronary heart disease: a machine learning-based diagnostic model integrating tongue parameters and blood metabolic biomarkers.

Purpose: This study aims to evaluate whether combining tongue manifestation parameters with blood metabolic biomarkers improves LASSO of carotid vulnerable plaques in patients with coronary heart disease (CHD), and to develop and compare the diagnostic performance of multiple machine-learning models. Methods: We retrospectively collected clinical data from 523 patients with CHD. Based on carotid ultrasound, patients were classified into vulnerable-plaque and non-vulnerable-plaque groups. We compared baseline characteristics, tongue manifestation parameters, and blood metabolic biomarkers between the two groups. The dataset was randomly split into training and validation sets at a 6:4 ratio. Factors associated with carotid vulnerable plaques in CHD were identified by intergroup comparison and LASSO regression, and four machine-learning algorithms were used to build predictive models. Results: Significant differences were found between the two groups in age, BMI,APOA1, FBG, AST, K Conclusion: There is a weak correlation between tongue image parameters and some blood metabolic markers, suggesting that the two may reflect the pathophysiological state of the body from different dimensions. Tongue image parameters, as non-invasive and simple assessment indicators, provide a preliminary exploratory reference for the risk stratification of coronary heart disease. However, their independent predictive value and clinical practicability still need to be further verified by prospective large-sample studies.